Collecting Measured Data

1. Collecting Measured Data Pre-Class Reading

2. Measurements • Engineers collect and use measurement data to analyze, create or verify the design of products or processes. • Correctly collecting and analyzing measured data are critical skills for engineers. • High quality data is VERY important and there are many possible pitfalls in dealing with measured data that must be understood and avoided.

3. Collecting Data • It is important to determine what needs to be measured before taking measurements. Engineering problems may require careful thought to determine what is most important characteristic to measure. • Typically in engineering, multiple measurements are collected because a single measurement rarely provides enough information

4. Collecting Data Example A coffee shop chain has received complaints at a number of their stores that their coffee has inconsistent temperature. The manager decides to hire an engineering consultant to determine what is causing this temperature variation.

5. Collecting Data Example An engineer goes to several stores to take measurements. One temperature measurement would not be sufficient to perform a proper analysis, so in order to fully understand the problem, many cups of coffee throughout the day have their temperatures measured at the stores. The sampling will provide the engineer with a large data sample that will help analyze the problem.

6. Collecting Data Example • After collecting the temperature data, it is seen that during peak selling times the measured temperature is much lower than other times during the day. (see graph) • By collecting multiple measurements throughout the day, the engineer is able to focus attention on the real problem - why the coffee machines are not working properly during peak selling hours and solve the problem.

7. Measurement System • Measured data is collected by people using some type of equipment. (an engineer using a thermometer in the coffee example) • This combination of human and instrumentation can be defined as a measurement system. • In studying measurement data we need to understand each component of the measurement system and its effect on the quality of the data.

8. Measured Data • Two important aspects of measured data: • The “expected value” (the coffee machine’s desired temperature) • The variation • Even when the coffee machine was working correctly, the measured coffee temperature will vary slightly from the desired coffee temperature

9. Variation • Variation in measured data is to be expected and is a natural aspect of the “real world” • When designing or evaluating a product, a range of acceptable variation needs to be established. It is the engineers job to establish this limit.

10. What Creates Variation? • To understand what creates variation we need to understand the following terms: • Accuracy • Repeatability (also known as Precision) • Resolution • Systematic Variation • Random Variation

11. Accuracy vs. Repeatability

12. Resolution • The resolution of an instrument is the smallest increment the tool displays or is capable of measuring reliably • Some electronic instruments will display values that are beyond their measurement resolution as show below: Electronic Bathroom Scale Example • Typically displays a value to the nearest tenth of a pound e.g. (170.6 lbs) • Inexpensive bathroom scales are not built well enough to measure to that resolution

13. Random vs. Systematic Variation Random Systematic (Unpredictable) (Predictable)

14. Random Variation Example • Mechanical and electrical instruments • Random variation can result from using instruments near the limits of their resolution

15. Random Variation Example Parallax Your eye needs to consistently be at position 2 (pictured) to get accurate readings off the ruler. If your eye position varies randomly between positions 1,2 and 3 for each reading, then your measurements will vary randomly. 1 2 3

16. Systematic Variation • Systematic Variation can be caused by human error and/or instrumentation error

17. Variation from Human Error Dial indicators can also easily be misread. Interchanging the large and small dial readings is a common error. Readings are composed of two parts: • Small hand: Keeps track of each revolution of the large hand, marks correspond to 0.100 inch • Large hand: Divided by 100 equally spaced marks that correspond to 0.001 inch

18. Systematic Variation from Human Error Example of not understanding how to correctly use measurement instrument • A new engineer is measuring the deflection of a beam with a dial indicator. However, the engineer is not familiar with how to correctly read the dial, and reverses the readings of the small hand with the large hand. All of the measured deflections are off by a similar magnitude.

19. Systematic Variation from Instrumentation Error Example of not zeroing a scale • If there is nothing on the scale, but it is not reading zero, this will create errors • Every measurement will be off by 0.02 g